Passive user verification through behavioral biometrics is the process of affirming the identity of a user based upon the user's unique, natural interactions with a system. Typical security measures focus only on authentication and, thus, are often easily compromised (e.g. loss of token or password) or expensive (e.g. retinal scanning hardware), or they place undue burden on the end-user (e.g. multi-factor authentication) that, in turn, elicits unsafe practices (e.g. leaving a machine unlocked to avoid reauthentication). In contrast, behavioral biometrics can enhance existing authentication mechanisms, such as passwords, while constantly or continually verifying the user after login. These methods can be similarly applied to digital forensics to identify an attacker who has gained access to stolen credentials or otherwise gained unlawful access (e.g. zero-day exploit).
Most modern behavioral biometric approaches rely on manually engineered (“handcrafted”) features to generate signatures to represent a user's unique patterns. These features are domain-specific and are based upon experience, intuition, and, often, trial and error. While these features may work well in controlled, laboratory experiments, many are fragile, overly complex, and fail to be robust in practice. Keystroke dynamics research has demonstrated these points by showing the decline in equal error rate (EER) for various keystroke algorithms when tested on more realistic (in that they better represent typical computer activity) datasets.
The advent of deep learning has given rise to viable automatic feature extraction methods that derive latent features from high-dimensional problem spaces with little-to-no domain knowledge. This approach has often proven to be more effective than traditional handcrafted features, revolutionizing aspects of computer vision, speech recognition, and artificial intelligence. Within the domain of biometrics, learning and feature selection have produced advances in facial and speaker recognition, yet these methods have not been applied to behavioral biometrics with the same pervasiveness or level of success.